Finite state machine implementation for left ventricle modeling and control.

BACKGROUND: Simulation of a left ventricle has become a critical facet of evaluating therapies and operations that interact with cardiac performance. The ability to simulate a wide range of possible conditions, changes in cardiac performance, and production of nuisances at transition points enables evaluation of precision medicine concepts that are designed to function through this spectrum. Ventricle models have historically been based on biomechanical analysis, with model architectures constituted of continuous states and not conducive to deterministic processing. Producing a finite-state machine governance of a left ventricle model would enable a broad range of applications: physiological controller development, experimental left ventricle control, and high throughput simulations of left ventricle function. METHODS: A method for simulating left ventricular pressure-volume control utilizing a preload, afterload, and contractility sensitive computational model is shown. This approach uses a logic-based conditional finite state machine based on the four pressure-volume phases that describe left ventricular function. This was executed with a physical system hydraulic model using MathWorks' Simulink® and Stateflow tools. RESULTS: The approach developed is capable of simulating changes in preload, afterload, and contractility in time based on a patient's preload analysis. Six pressure-volume loop simulations are presented to include a base-line, preload change only, afterload change only, contractility change only, a clinical control, and heart failure with normal ejection fraction. All simulations produced an error of less than 1 mmHg and 1 mL of the absolute difference between the desired and simulated pressure and volume set points. The acceptable performance of the fixed-timestep architecture in the finite state machine allows for deployment to deterministic systems, such as experimental systems for validation. CONCLUSIONS: The proposed approach allows for personalized data, revealed through an individualized clinical pressure-volume analysis, to be simulated in silico. The computational model architecture enables this control structure to be executed on deterministic systems that govern experimental left ventricles. This provides a mock circulatory system with the ability to investigate the pathophysiology for a specific individual by replicating the exact pressure-volume relationship defined by their left ventricular functionality; as well as perform predictive analysis regarding changes in preload, afterload, and contractility in time.

Development of an adaptive pulmonary simulator for in vitro analysis of patient populations and patient-specific data.

BACKGROUND AND OBJECTIVE: Patient-specific modeling (PSM) is gaining more attention from researchers due to its ability to potentially improve diagnostic capabilities, guide the design of intervention procedures, and optimize clinical management by predicting the outcome of a particular treatment and/or surgical intervention. Due to the hemodynamic diversity of specific patients, an adaptive pulmonary simulator (PS) would be essential for analyzing the possible impact of external factors on the safety, performance, and reliability of a cardiac assist device within a mock circulatory system (MCS). In order to accurately and precisely replicate the conditions within the pulmonary system, a PS should not only account for the ability of the pulmonary system to supply blood flow at specific pressures, but similarly consider systemic outflow dynamics. This would provide an accurate pressure and flow rate return supply back into the left ventricular section of the MCS (i.e. the initial conditions of the left heart). METHODS: Employing an embedded Windkessel model, a control system model was developed utilizing MathWorks' Simulink® Simscape™. Following a verification and validation (V&V) analysis approach, a PI-controlled closed-loop hydraulic system was developed using Simscape™. This physical system modeling tool was used to (1) develop and control the in silico system during verification studies and (2) simulate pulmonary performance for validation of this control architecture. RESULTS: The pulmonary Windkessel model developed is capable of generating the left atrial pressure (LAP) waveform from given pulmonary factors, aortic conditions, and systemic variables. Verification of the adaptive PS's performance and validation of this control architecture support this modeling methodology as an effective means of reproducing pulmonary pressure waveforms and systemic outflow conditions, unique to a particular patient. Adult and geriatric with and without Heart Failure and a Normal Ejection Fraction (HFNEF) are presented. CONCLUSIONS: The adaptability of this modelling approach allows for the simulation of pulmonary conditions without the limitations of a dedicated hardware platform for use in in vitro investigations.